In contrast, detect-and-describe approaches advance description stage. By sharing a representation from deep neural network, SuperPoint [1]}, UnSuperPoint [2]} and R2D2 [3]} attempt to learn a dense feature descriptor and a feature detector. However, they rely on different decoder branches which are trained independently with specific losses. On the contrary, D2-net [4]} and ASLFeat [5]} shares all parameters between detection and description and uses a joint formulation that simultaneously optimizes for both tasks.
